Gl-learning: an optimized framework for grammatical inference

Cottone, Pietro; Ortolani, Marco; Pergola, Gabriele

Abstract

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application scenarios for grammar models. The modular design of our C++ library makes it an efficient and extensible framework for the design of further novel algorithms.

Citation

Cottone, P., Ortolani, M., & Pergola, G. (2016, June). Gl-learning: an optimized framework for grammatical inference. Presented at CompSysTech '16: Computer Systems and Technologies 2016, Palermo Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: CompSysTech '16: Computer Systems and Technologies 2016
• Start Date: Jun 23, 2016
• End Date: Jun 24, 2016
• Online Publication Date: Jun 23, 2016
• Publication Date: Jun 23, 2016
• Deposit Date: Dec 14, 2023
• Publisher: Association for Computing Machinery (ACM)
• Book Title: CompSysTech '16: Computer Systems and Technologies 2016
• ISBN: 978-1-4503-4182-0
• DOI: https://doi.org/10.1145/2983468.2983502
• Public URL: https://keele-repository.worktribe.com/output/668175
• Additional Information: Published: 2016-06-23